Extremely Large Telescope

Abstract:

We present results on the structural properties of massive passive galaxies in three clusters at 1.39 < z < 1.61 from the KMOS Cluster Survey. We measure light-weighted and mass-weighted sizes from optical and near-infrared Hubble Space Telescope imaging and spatially resolved stellar mass maps. The rest-frame R-band sizes of these galaxies are a factor of ∼2-3 smaller than their local counterparts. The slopes of the relation between the stellar mass and the light-weighted size are consistent with recent studies in clusters and the field. Their mass-weighted sizes are smaller than the rest-frame R-band sizes, with an average mass-weighted to light-weighted size ratio that varies between ∼0.45 and 0.8 among the clusters. We find that the median light-weighted size of the passive galaxies in the two more evolved clusters is ∼24% larger than that for field galaxies, independent of the use of circularized effective radii or semimajor axes. These two clusters also show a smaller size ratio than the less evolved cluster, which we investigate using color gradients to probe the underlying gradients. The median color gradients are ∇z - H ∼ -0.4 mag dex -1 , twice the local value. Using stellar populations models, these gradients are best reproduced by a combination of age and metallicity gradients. Our results favor the minor merger scenario as the dominant process responsible for the observed galaxy properties and the environmental differences at this redshift. The environmental differences support that clusters experience accelerated structural evolution compared to the field, likely via an epoch of enhanced minor merger activity during cluster assembly.

Abstract:

Microwave Kinetic Inductance Detector (MKID) devices offer inherent spectral resolution, simultaneous read out of thousands of pixels, and photon-limited sensitivity at optical wavelengths. Before taking observations the readout power and frequency of each pixel must be individually tuned, and if the equilibrium state of the pixels change, then the readout must be retuned. This process has previously been performed through manual inspection, and typically takes one hour per 500 resonators (20 h for a ten-kilo-pixel array). We present an algorithm based on a deep convolution neural network (CNN) architecture to determine the optimal bias power for each resonator. The bias point classifications from this CNN model, and those from alternative automated methods, are compared to those from human decisions, and the accuracy of each method is assessed. On a test feed-line dataset, the CNN achieves an accuracy of 90% within 1 dB of the designated optimal value, which is equivalent accuracy to a randomly selected human operator, and superior to the highest scoring alternative automated method by 10%. On a full ten-kilopixel array, the CNN performs the characterization in a matter of minutes — paving the way for future mega-pixel MKID arrays.